Fresh research by INSEAD faculty examines how peer quality influences performance evaluations, and what happens when activists get involved in CEO recruiting. Two more new papers explore the impact of household earnings on the mental health of young children and how the meaning one attaches to their work affects their ability to change careers. The two final papers propose novel approaches to knowledge-weighting and solving optimisation problems.
1. Peer performance affects employee ratings
The quality of your peers – how capable your colleagues or fellow employees are – impacts your performance evaluations. That is the conclusion of Gavin Cassar from INSEAD and Taeho Ko of Hanyang University after conducting a study analysing 75,413 ratings of 130 employees from 6,908 raters within a business school setting.
They found that employees with higher quality peer groups receive lower subjective performance ratings. The authors further observed that the peer effect is stronger for employees who perform poorly, have more similar peers, or work with colleagues whose performance stands out.
2. How activist shareholders influence CEO hiring
CEOs who are recruited with the influence of shareholder activists are associated with better stock market reactions and greater profitability improvements compared to those appointed without such influence. That’s according to research by INSEAD’s Thomas Keusch. Conversely, he finds there is little evidence that shareholder influence leads to the recruitment of CEOs who implement short-sighted corporate policies.
Further analyses revealed that when activists are involved, companies dedicate more time and energy to the CEO search process and are more inclined to hire CEOs from external sources.
3. The impact of household earnings on children’s mental health
Income shocks have a lasting influence on children’s mental health. Research by INSEAD’s Mark Stabile and co-authors* investigated the impact of household earnings shocks on the proportion of children who received a designation for a moderate or severe mental health condition during the Great Recession of 2007 to 2009.
They found that, relative to children who were not affected by recession-induced earnings drops, the rate of new mental health designations among children with earnings losses was 0.5 percentage points higher (20 percent) during the recession. The effect of experiencing a recessionary earning loss persisted and intensified, particularly for children who faced these losses at age 10 or younger.
*Lauren Jones, The Ohio State University, Kourtney Koebel, University of Toronto, and Jill Furzer, Keystone Strategy
Read the full paper
4. The power of flexible meaning in navigating career disruption
Individuals react differently to career upheaval: Some struggle to transition to a new profession while others switch jobs with ease. Research by INSEAD’s Winnie Jiang and her co-author Amy Wrzesniewski of The Wharton School explored these two sets of responses to job loss.
Through interviews with 72 former journalists, they found that those who saw the meaning they derived from their work as “flexible” were able to seek new careers. Meanwhile, those who viewed the meaning of their work to be “fixed” to one occupational context tried to persist in journalism.
5. A novel knowledge-weighting approach
INSEAD’s Ville Satopää and Asa Palley of the Kelley School of Business at Indiana University propose a weighting of experts’ (judges) individual predictions that better combines their collective feedback within a single estimation. Each judge is asked to provide both their own estimation and their prediction of the average estimate that will be given by all the other judges.
Predictions of the others are then used as part of a benchmark to determine what weighting should be applied to each judge’s estimate to form a much-improved aggregate estimate, compared to existing methods. As the authors put it, a crowd that predicts others well can be expected to have a more accurate estimate of the target variable.
6. A new approach to solving optimisation problems
Solving optimisation problems – finding the best solution among many – is crucial in various industries, especially in the field of machine learning, where “training a model” often involves solving large-scale problems.
In this paper, INSEAD’s Georgina Hall and her co-authors* consider a specific type of optimisation problem, one known to be very computationally intensive to solve. They investigate how assuming additional structure on the problem can make it much cheaper to solve, among other takeaways.
*Amir Ali Ahmadi, Princeton, and Cemil Dibek, Princeton
Edited by:INSEAD Knowledge
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